Exploring Protein-Protein and Protein-Ligand Interactions in the Immune System using Molecular Dynamics and Continuum Electrostatics

2012 ◽  
Vol 2 (4) ◽  
pp. 324-343 ◽  
Author(s):  
Chris A. Kieslich ◽  
Phanourios Tamamis ◽  
Ronald D. Gorham Jr. ◽  
Aliana Lopez de Victoria ◽  
Noriko U. Sausman ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Immune System ◽  
Continuum Electrostatics ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

Exploring Protein-Protein and Protein-Ligand Interactions in the Immune System using Molecular Dynamics and Continuum Electrostatics

2012 ◽  
Vol 2 (4) ◽  
pp. 324-343 ◽  
Author(s):  
Chris A. Kieslich ◽  
Phanourios Tamamis ◽  
Ronald D. Gorham Jr. ◽  
Aliana Lopez de Victoria ◽  
Noriko U. Sausman ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Immune System ◽  
Continuum Electrostatics ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

Using molecular docking and molecular dynamics to investigate protein-ligand interactions

2021 ◽  
Vol 35 (08) ◽  
pp. 2130002
Author(s):  
Connor J. Morris ◽  
Dennis Della Corte
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Drug Discovery ◽  
State Of The Art ◽  
Ligand Complex ◽  
Protein Ligand Interactions ◽  
Art Methods ◽  
Ligand Interactions ◽  
Docking Calculations ◽  
Ligand Bond

Molecular docking and molecular dynamics (MD) are powerful tools used to investigate protein-ligand interactions. Molecular docking programs predict the binding pose and affinity of a protein-ligand complex, while MD can be used to incorporate flexibility into docking calculations and gain further information on the kinetics and stability of the protein-ligand bond. This review covers state-of-the-art methods of using molecular docking and MD to explore protein-ligand interactions, with emphasis on application to drug discovery. We also call for further research on combining common molecular docking and MD methods.

scholarly journals Enlighten2: molecular dynamics simulations of protein–ligand systems made accessible

2020 ◽  
Vol 36 (20) ◽  
pp. 5104-5106
Author(s):  
Kirill Zinovjev ◽  
Marc W van der Kamp
Keyword(s):  
Molecular Dynamics ◽  
Expert Knowledge ◽  
Structural Data ◽  
Md Simulations ◽  
Enzyme Engineering ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Dynamics Simulations ◽  
User Friendly ◽  
Dynamical Information

Abstract Motivation Experimental structural data can allow detailed insight into protein structure and protein–ligand interactions, which is crucial for many areas of bioscience, including drug design and enzyme engineering. Typically, however, little more than a static picture of protein–ligand interactions is obtained, whereas dynamical information is often required for deeper understanding and to assess the effect of mutations. Molecular dynamics (MD) simulations can provide such information, but setting up and running these simulations is not straightforward and requires expert knowledge. There is thus a need for a tool that makes protein–ligand simulation easily accessible to non-expert users. Results We present Enlighten2: efficient simulation protocols for protein–ligand systems alongside a user-friendly plugin to the popular visualization program PyMOL. With Enlighten2, non-expert users can straightforwardly run and visualize MD simulations on protein–ligand models of interest. There is no need to learn new programs and all underlying tools are free and open source. Availability and implementation The Enlighten2 Python package and PyMOL plugin are free to use under the GPL3.0 licence and can be found at https://enlighten2.github.io. We also provide a lightweight Docker image via DockerHub that includes Enlighten2 with all the required utilities.

scholarly journals An Investigation of Small GTPases in relation to Liver Tumorigenesis Using Traditional Chinese Medicine

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Tzu-Chieh Hung ◽  
Wen-Yuan Lee ◽  
Kuen-Bao Chen ◽  
Yueh-Chiu Chan ◽  
Calvin Yu-Chian Chen
Keyword(s):  
Molecular Dynamics ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Hydrophobic Interactions ◽  
Small Gtpases ◽  
Ligand Interaction ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Protein Ligand Interaction ◽  
Better Than

Recently, an important topic of liver tumorigenesis had been published in 2013. In this report, Ras and Rho had defined the relation of liver tumorigenesis. The traditional Chinese medicine (TCM) database has been screened for molecular compounds by simulating molecular docking and molecular dynamics to regulate Ras and liver tumorigenesis. Saussureamine C, S-allylmercaptocysteine, and Tryptophan are selected based on the highest docking score than other TCM compounds. The molecular dynamics are helpful in the analysis and detection of protein-ligand interactions. Based on the docking poses, hydrophobic interactions, and hydrogen bond variations, this research surmises are the main regions of important amino acids in Ras. In addition to the detection of TCM compound efficacy, we suggest Saussureamine C is better than the others for protein-ligand interaction.

Molecular Dynamics in Mixed Solvents Reveals Protein–Ligand Interactions, Improves Docking, and Allows Accurate Binding Free Energy Predictions

2017 ◽  
Vol 57 (4) ◽  
pp. 846-863 ◽  
Author(s):  
Juan Pablo Arcon ◽  
Lucas A. Defelipe ◽  
Carlos P. Modenutti ◽  
Elias D. López ◽  
Daniel Alvarez-Garcia ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Binding Free Energy ◽  
Mixed Solvents ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

scholarly journals Identification of Interactions of ABT-341 to Dipeptidyl Peptidase IV during Molecular Dynamics Simulations

2021 ◽  
Vol 7 (2) ◽  
pp. 91-98
Author(s):  
Enade Istyastono ◽  
Michael Gani
Keyword(s):  
Molecular Dynamics ◽  
Design Research ◽  
Hydrophobic Interactions ◽  
Md Simulations ◽  
Dipeptidyl Peptidase ◽  
Dipeptidyl Peptidase Iv ◽  
Structure Based Drug Design ◽  
Protein Ligand Interactions ◽  
Dpp Iv ◽  
Ligand Interactions

Background: Dipeptidyl Peptidase IV (DPP-IV) is an established drug discovery target for type 2 diabetes mellitus (T2DM) therapy. On the other hand, molecular dynamics (MD) simulations have been widely employed to obtain insights of the protein-ligand interactions in structure-based drug design research projects. Moreover, a software to identify protein-ligand interactions called PyPLIF HIPPOS was made publicly available recently. Employing PyPLIF HIPPOS to identify the interactions of DPP-IV and its ligand ABT-341 during MD simulations was then of considerable interest. Objectives: The main aim of this study was to identify protein-ligand interactions of ABT-341 to DPP-IV during MD simulations. Material and Methods: The crystal structure of DPP-IV co-crystallized with ABT-341 obtained from the Protein Data Bank with code of 2I78 was used as the main material. YASARA-Structure was employed for performing 10 ns prodution run MD simulations with snapshots in every 100 ps and PyPLIF HIPPOS was used to identify the protein-ligand interactions. Results: There were 23 interactions involving 13 residues identified by employing PyPLIF HIPPOS during the MD simulations. Two of them identified in all snapshots, i.e., hydrophobic interactions to PHE357 and TYR666. Conclusions: PyPLIF HIPPOS was succesfully employed to identify the interactions of ABT-341 to DPP-IV during MD simulations.

scholarly journals Insights into the substrate binding mechanism of SULT1A1 through molecular dynamics with excited normal modes simulations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Balint Dudas ◽  
Daniel Toth ◽  
David Perahia ◽  
Arnaud B. Nicot ◽  
Erika Balog ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Conformational Changes ◽  
Molecular Mechanisms ◽  
Normal Modes ◽  
Md Simulations ◽  
Conformational Space ◽  
Metabolizing Enzymes ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Excited Normal Modes

AbstractSulfotransferases (SULTs) are phase II drug-metabolizing enzymes catalyzing the sulfoconjugation from the co-factor 3′-phosphoadenosine 5′-phosphosulfate (PAPS) to a substrate. It has been previously suggested that a considerable shift of SULT structure caused by PAPS binding could control the capability of SULT to bind large substrates. We employed molecular dynamics (MD) simulations and the recently developed approach of MD with excited normal modes (MDeNM) to elucidate molecular mechanisms guiding the recognition of diverse substrates and inhibitors by SULT1A1. MDeNM allowed exploring an extended conformational space of PAPS-bound SULT1A1, which has not been achieved up to now by using classical MD. The generated ensembles combined with docking of 132 SULT1A1 ligands shed new light on substrate and inhibitor binding mechanisms. Unexpectedly, our simulations and analyses on binding of the substrates estradiol and fulvestrant demonstrated that large conformational changes of the PAPS-bound SULT1A1 could occur independently of the co-factor movements that could be sufficient to accommodate large substrates as fulvestrant. Such structural displacements detected by the MDeNM simulations in the presence of the co-factor suggest that a wider range of drugs could be recognized by PAPS-bound SULT1A1 and highlight the utility of including MDeNM in protein–ligand interactions studies where major rearrangements are expected.

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